Overview
The Custom Recommendation Engine for personalized content suggestions uses deep learning techniques to track students' interactions with learning materials over time. The solution provides valuable insights into students' learning paths and progress, allowing the implementation of a personalized recommendation engine to provide customers with unique and tailored experiences.
With the help of our solution, it becomes easy to achieve:
- Highly relevant content suggestions
- Content-based filtering
- Scalability and real-time recommendations
- Data privacy and security
Using AWS's advanced capabilities, such as secure and scalable storage with Amazon S3, efficient computing with AWS Lambda, and cutting-edge deep learning models with Amazon SageMaker, our solution provides accurate knowledge tracing and personalized recommendations for each student.
Highlights
- A high envisioned knowledge tracing accuracy of over 80% significantly enhances educational outcomes by providing precise insights into student progress, facilitating targeted interventions, and enabling personalized learning experiences.
- The model has helped the business achieve 75% cost savings over 5 years.
Details
Pricing
Custom pricing options
Legal
Content disclaimer
Resources
Vendor resources
Support
Vendor support
Reach out to:
Jim Keller AWS Channel Global Leader, Quantiphi Email- aws.programs@quantiphi.com
Website: